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   "source": [
    "# 第3讲：RNN时间序列预测\n",
    "## Step1: 模拟生成时间序列\n",
    "> _基线序列 + 直线趋势序列 + 周期性趋势序列 + 呈正态分布的误差_"
   ],
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   "source": [
    "# 导入\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "\n",
    "plt.style.use(['science', 'grid', 'muted'])"
   ]
  },
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   "cell_type": "code",
   "execution_count": 2,
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "def plot_series(times, series, label=None):\n",
    "    \"\"\"绘图\"\"\"\n",
    "    plt.plot(times, series, label=label)\n",
    "    plt.xlabel('time')\n",
    "    plt.ylabel('value')\n",
    "\n",
    "def trend(time, slope=0): \n",
    "    \"\"\"趋势性\"\"\"\n",
    "    return slope * time\n",
    "    \n",
    "def seasonality(time, period, amplitude=1, phase=0): \n",
    "    \"\"\"周期性\"\"\"\n",
    "    seasonal_pattern = lambda season_time: np.where(season_time < 0.4, np.cos(season_time * 2 * np.pi), 1 / np.exp(3 * season_time)) \n",
    "    season_time = (time + phase) % period / period\n",
    "    return amplitude * seasonal_pattern(season_time)\n",
    "\n",
    "def noise(time, noise_level=1, seed=None):\n",
    "    \"\"\"噪声\"\"\"\n",
    "    rnd = np.random.RandomState(seed)\n",
    "    return rnd.randn(len(time)) * noise_level\n",
    "\n",
    "time = np.arange(4 * 365 + 1, dtype='float32') # 以一年为1个周期，生成4个周期\n",
    "baseline = 10 # 基线\n",
    "amplitude = 40 # 振幅\n",
    "slope = 0.05 # 斜率\n",
    "noise_level  = 5 # 噪声水平\n",
    "\n",
    "# 创建序列\n",
    "series = baseline + trend(time, slope) + seasonality(time, period=365, amplitude=amplitude) + noise(time, noise_level, seed=42)\n",
    "\n",
    "# 划分序列\n",
    "split_time = 1000\n",
    "time_train = time[:split_time]\n",
    "X_train =  series[:split_time]\n",
    "time_valid = time[split_time:]\n",
    "X_valid =  series[split_time:]\n",
    "\n",
    "# 可视化\n",
    "plt.figure(figsize=(10, 6))\n",
    "plot_series(time_train, X_train, 'train')\n",
    "plot_series(time_valid, X_valid, 'valid')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "source": [
    "## Step2: 生成RNN输入样本\n",
    "> _通过加窗的方式将训练集转化生成神经网络的输入数据_"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "window_size = 20\n",
    "batch_size = 32\n",
    "shuffle_buffer_size = 1000\n",
    "\n",
    "def windowed_dataset(series, window_size, batch_size, shuffle_buffer):\n",
    "    \"\"\"模拟生成数据集\n",
    "    \n",
    "    ---Parameter---\n",
    "    series: 序列数据\n",
    "\n",
    "    window_size：窗口大小\n",
    "\n",
    "    batch_size：批次大小\n",
    "\n",
    "    \n",
    "    shuffle_buffer： 随机缓存大小\n",
    "    \"\"\"\n",
    "    dataset = tf.data.Dataset.from_tensor_slices(series) # 切分序列\n",
    "    dataset = dataset.window(window_size + 1, shift=1, drop_remainder=True) # 加窗\n",
    "    dataset = dataset.flat_map(lambda window: window.batch(window_size + 1)) # 将序列划为窗口\n",
    "    dataset = dataset.shuffle(shuffle_buffer).map(lambda window: (window[:-1], window[-1]), num_parallel_calls=12 * 2) # 打乱并划分特征和标签（在多个 CPU 核心上并行地进行预处理）\n",
    "    dataset = dataset.batch(batch_size).prefetch(1) # 分批\n",
    "    return dataset\n",
    "\n",
    "train_set = windowed_dataset(X_train, window_size, batch_size=batch_size, shuffle_buffer=shuffle_buffer_size)"
   ]
  },
  {
   "source": [
    "## Step3: 定义SimpleRNN神经网络"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 搭建SimpleRNN神经网络，使用LR_scheduler机制调整学习率\n",
    "tf.keras.backend.clear_session() # 销毁当前的TF图并创建一个新图, 有助于避免旧模型/图层混乱\n",
    "tf.random.set_seed(51)\n",
    "\n",
    "model = tf.keras.models.Sequential([\n",
    "    tf.keras.layers.Lambda(lambda x: tf.expand_dims(x, axis=-1), input_shape=[None]),\n",
    "    tf.keras.layers.SimpleRNN(40, return_sequences=True), \n",
    "    tf.keras.layers.SimpleRNN(40),\n",
    "    tf.keras.layers.Dense(1),\n",
    "    tf.keras.layers.Lambda(lambda x: x * 100.0)    \n",
    "])\n"
   ]
  },
  {
   "source": [
    "## Step4: 定义神经网络的参数\n",
    "> _优化器、损失函数和度量指标_\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr_schedule = tf.keras.callbacks.LearningRateScheduler(lambda epoch: 1e-8 * 10**(epoch / 20))\n",
    "\n",
    "model.compile(\n",
    "    loss=tf.keras.losses.Huber(),\n",
    "    optimizer=tf.keras.optimizers.SGD(lr=1e-8, momentum=0.9),\n",
    "    metrics=[tf.keras.metrics.MeanAbsoluteError()]\n",
    ")"
   ]
  },
  {
   "source": [
    "## Step5: RNN进行神经网络模型的训练"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Epoch 1/100\n",
      "WARNING:tensorflow:From /home/xuhaowei/anaconda3/envs/tf/lib/python3.6/site-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
      "31/31 [==============================] - 3s 112ms/step - loss: 54.0808 - mean_absolute_error: 54.5388\n",
      "Epoch 2/100\n",
      "31/31 [==============================] - 1s 48ms/step - loss: 51.6078 - mean_absolute_error: 52.0720\n",
      "Epoch 3/100\n",
      "31/31 [==============================] - 1s 46ms/step - loss: 48.6540 - mean_absolute_error: 49.1211\n",
      "Epoch 4/100\n",
      "31/31 [==============================] - 1s 48ms/step - loss: 45.3923 - mean_absolute_error: 45.8625\n",
      "Epoch 5/100\n",
      "31/31 [==============================] - 1s 48ms/step - loss: 41.8346 - mean_absolute_error: 42.3088\n",
      "Epoch 6/100\n",
      "31/31 [==============================] - 2s 50ms/step - loss: 37.9812 - mean_absolute_error: 38.4587\n",
      "Epoch 7/100\n",
      "31/31 [==============================] - 2s 51ms/step - loss: 33.8871 - mean_absolute_error: 34.3688\n",
      "Epoch 8/100\n",
      "31/31 [==============================] - 2s 51ms/step - loss: 29.6428 - mean_absolute_error: 30.1297\n",
      "Epoch 9/100\n",
      "31/31 [==============================] - 1s 46ms/step - loss: 25.3500 - mean_absolute_error: 25.8434\n",
      "Epoch 10/100\n",
      "31/31 [==============================] - 2s 49ms/step - loss: 21.3670 - mean_absolute_error: 21.8599\n",
      "Epoch 11/100\n",
      "31/31 [==============================] - 1s 46ms/step - loss: 18.4732 - mean_absolute_error: 18.9693\n",
      "Epoch 12/100\n",
      "31/31 [==============================] - 1s 47ms/step - loss: 16.7494 - mean_absolute_error: 17.2412\n",
      "Epoch 13/100\n",
      "31/31 [==============================] - 1s 47ms/step - loss: 15.6957 - mean_absolute_error: 16.1863\n",
      "Epoch 14/100\n",
      "31/31 [==============================] - 1s 47ms/step - loss: 14.9912 - mean_absolute_error: 15.4835\n",
      "Epoch 15/100\n",
      "31/31 [==============================] - 1s 46ms/step - loss: 14.4830 - mean_absolute_error: 14.9683\n",
      "Epoch 16/100\n",
      "31/31 [==============================] - 1s 44ms/step - loss: 14.1136 - mean_absolute_error: 14.6003\n",
      "Epoch 17/100\n",
      "31/31 [==============================] - 1s 46ms/step - loss: 13.8070 - mean_absolute_error: 14.2932\n",
      "Epoch 18/100\n",
      "31/31 [==============================] - 2s 50ms/step - loss: 13.5197 - mean_absolute_error: 14.0043\n",
      "Epoch 19/100\n",
      "31/31 [==============================] - 1s 48ms/step - loss: 13.2365 - mean_absolute_error: 13.7203\n",
      "Epoch 20/100\n",
      "31/31 [==============================] - 1s 46ms/step - loss: 12.9476 - mean_absolute_error: 13.4299\n",
      "Epoch 21/100\n",
      "31/31 [==============================] - 1s 47ms/step - loss: 12.6466 - mean_absolute_error: 13.1276\n",
      "Epoch 22/100\n",
      "31/31 [==============================] - 1s 45ms/step - loss: 12.3300 - mean_absolute_error: 12.8115\n",
      "Epoch 23/100\n",
      "31/31 [==============================] - 1s 45ms/step - loss: 11.9934 - mean_absolute_error: 12.4758\n",
      "Epoch 24/100\n",
      "31/31 [==============================] - 2s 51ms/step - loss: 11.6320 - mean_absolute_error: 12.1136\n",
      "Epoch 25/100\n",
      "31/31 [==============================] - 2s 51ms/step - loss: 11.2406 - mean_absolute_error: 11.7204\n",
      "Epoch 26/100\n",
      "31/31 [==============================] - 1s 48ms/step - loss: 10.8204 - mean_absolute_error: 11.3009\n",
      "Epoch 27/100\n",
      "31/31 [==============================] - 1s 45ms/step - loss: 10.3715 - mean_absolute_error: 10.8491\n",
      "Epoch 28/100\n",
      "31/31 [==============================] - 2s 49ms/step - loss: 9.8949 - mean_absolute_error: 10.3715\n",
      "Epoch 29/100\n",
      "31/31 [==============================] - 2s 49ms/step - loss: 9.3946 - mean_absolute_error: 9.8742\n",
      "Epoch 30/100\n",
      "31/31 [==============================] - 2s 49ms/step - loss: 8.8673 - mean_absolute_error: 9.3450\n",
      "Epoch 31/100\n",
      "31/31 [==============================] - 1s 47ms/step - loss: 8.3280 - mean_absolute_error: 8.8009\n",
      "Epoch 32/100\n",
      "31/31 [==============================] - 2s 48ms/step - loss: 7.8163 - mean_absolute_error: 8.2865\n",
      "Epoch 33/100\n",
      "31/31 [==============================] - 2s 49ms/step - loss: 7.3334 - mean_absolute_error: 7.7993\n",
      "Epoch 34/100\n",
      "31/31 [==============================] - 1s 47ms/step - loss: 6.9042 - mean_absolute_error: 7.3696\n",
      "Epoch 35/100\n",
      "31/31 [==============================] - 1s 48ms/step - loss: 6.5310 - mean_absolute_error: 6.9954\n",
      "Epoch 36/100\n",
      "31/31 [==============================] - 1s 46ms/step - loss: 6.2596 - mean_absolute_error: 6.7243\n",
      "Epoch 37/100\n",
      "31/31 [==============================] - 1s 48ms/step - loss: 6.0934 - mean_absolute_error: 6.5603\n",
      "Epoch 38/100\n",
      "31/31 [==============================] - 1s 46ms/step - loss: 5.9932 - mean_absolute_error: 6.4614\n",
      "Epoch 39/100\n",
      "31/31 [==============================] - 1s 45ms/step - loss: 5.9818 - mean_absolute_error: 6.4574\n",
      "Epoch 40/100\n",
      "31/31 [==============================] - 1s 46ms/step - loss: 5.9979 - mean_absolute_error: 6.4760\n",
      "Epoch 41/100\n",
      "31/31 [==============================] - 1s 47ms/step - loss: 5.9109 - mean_absolute_error: 6.3869\n",
      "Epoch 42/100\n",
      "31/31 [==============================] - 1s 45ms/step - loss: 5.5893 - mean_absolute_error: 6.0579\n",
      "Epoch 43/100\n",
      "31/31 [==============================] - 1s 46ms/step - loss: 5.5318 - mean_absolute_error: 6.0040\n",
      "Epoch 44/100\n",
      "31/31 [==============================] - 1s 47ms/step - loss: 5.5655 - mean_absolute_error: 6.0406\n",
      "Epoch 45/100\n",
      "31/31 [==============================] - 2s 49ms/step - loss: 5.5864 - mean_absolute_error: 6.0598\n",
      "Epoch 46/100\n",
      "31/31 [==============================] - 1s 48ms/step - loss: 5.4638 - mean_absolute_error: 5.9261\n",
      "Epoch 47/100\n",
      "31/31 [==============================] - 1s 48ms/step - loss: 5.5663 - mean_absolute_error: 6.0264\n",
      "Epoch 48/100\n",
      "31/31 [==============================] - 1s 44ms/step - loss: 5.6475 - mean_absolute_error: 6.1026\n",
      "Epoch 49/100\n",
      "31/31 [==============================] - 1s 46ms/step - loss: 5.6173 - mean_absolute_error: 6.0820\n",
      "Epoch 50/100\n",
      "31/31 [==============================] - 1s 47ms/step - loss: 5.7136 - mean_absolute_error: 6.1831\n",
      "Epoch 51/100\n",
      "31/31 [==============================] - 1s 47ms/step - loss: 5.6570 - mean_absolute_error: 6.1292\n",
      "Epoch 52/100\n",
      "31/31 [==============================] - 2s 49ms/step - loss: 5.5267 - mean_absolute_error: 5.9920\n",
      "Epoch 53/100\n",
      "31/31 [==============================] - 1s 48ms/step - loss: 5.4894 - mean_absolute_error: 5.9578\n",
      "Epoch 54/100\n",
      "31/31 [==============================] - 1s 48ms/step - loss: 5.6692 - mean_absolute_error: 6.1345\n",
      "Epoch 55/100\n",
      "31/31 [==============================] - 1s 48ms/step - loss: 5.7912 - mean_absolute_error: 6.2597\n",
      "Epoch 56/100\n",
      "31/31 [==============================] - 1s 45ms/step - loss: 5.8561 - mean_absolute_error: 6.3166\n",
      "Epoch 57/100\n",
      "31/31 [==============================] - 1s 47ms/step - loss: 6.0748 - mean_absolute_error: 6.5469\n",
      "Epoch 58/100\n",
      "31/31 [==============================] - 1s 46ms/step - loss: 5.5037 - mean_absolute_error: 5.9535\n",
      "Epoch 59/100\n",
      "31/31 [==============================] - 1s 47ms/step - loss: 6.9160 - mean_absolute_error: 7.4045\n",
      "Epoch 60/100\n",
      "31/31 [==============================] - 1s 47ms/step - loss: 5.9249 - mean_absolute_error: 6.3669\n",
      "Epoch 61/100\n",
      "31/31 [==============================] - 2s 49ms/step - loss: 6.6163 - mean_absolute_error: 7.0730\n",
      "Epoch 62/100\n",
      "31/31 [==============================] - 1s 45ms/step - loss: 7.2659 - mean_absolute_error: 7.7481\n",
      "Epoch 63/100\n",
      "31/31 [==============================] - 1s 48ms/step - loss: 5.8171 - mean_absolute_error: 6.2733\n",
      "Epoch 64/100\n",
      "31/31 [==============================] - 1s 47ms/step - loss: 7.9739 - mean_absolute_error: 8.4680\n",
      "Epoch 65/100\n",
      "31/31 [==============================] - 1s 46ms/step - loss: 6.2025 - mean_absolute_error: 6.6643\n",
      "Epoch 66/100\n",
      "31/31 [==============================] - 1s 46ms/step - loss: 5.8410 - mean_absolute_error: 6.3213\n",
      "Epoch 67/100\n",
      "31/31 [==============================] - 1s 46ms/step - loss: 7.0964 - mean_absolute_error: 7.5691\n",
      "Epoch 68/100\n",
      "31/31 [==============================] - 2s 50ms/step - loss: 6.5339 - mean_absolute_error: 6.9662\n",
      "Epoch 69/100\n",
      "31/31 [==============================] - 2s 51ms/step - loss: 8.7389 - mean_absolute_error: 9.2206\n",
      "Epoch 70/100\n",
      "31/31 [==============================] - 2s 51ms/step - loss: 6.7996 - mean_absolute_error: 7.2126\n",
      "Epoch 71/100\n",
      "31/31 [==============================] - 2s 49ms/step - loss: 8.0266 - mean_absolute_error: 8.4881\n",
      "Epoch 72/100\n",
      "31/31 [==============================] - 1s 46ms/step - loss: 7.3156 - mean_absolute_error: 7.7868\n",
      "Epoch 73/100\n",
      "31/31 [==============================] - 1s 47ms/step - loss: 7.3374 - mean_absolute_error: 7.8121\n",
      "Epoch 74/100\n",
      "31/31 [==============================] - 2s 48ms/step - loss: 9.6377 - mean_absolute_error: 10.1230\n",
      "Epoch 75/100\n",
      "31/31 [==============================] - 2s 49ms/step - loss: 9.4352 - mean_absolute_error: 9.8587\n",
      "Epoch 76/100\n",
      "31/31 [==============================] - 2s 49ms/step - loss: 14.0022 - mean_absolute_error: 14.4447\n",
      "Epoch 77/100\n",
      "31/31 [==============================] - 1s 46ms/step - loss: 11.4754 - mean_absolute_error: 11.9304\n",
      "Epoch 78/100\n",
      "31/31 [==============================] - 1s 48ms/step - loss: 13.6937 - mean_absolute_error: 14.2291\n",
      "Epoch 79/100\n",
      "31/31 [==============================] - 1s 46ms/step - loss: 18.5739 - mean_absolute_error: 19.0567\n",
      "Epoch 80/100\n",
      "31/31 [==============================] - 2s 50ms/step - loss: 15.5998 - mean_absolute_error: 16.1503\n",
      "Epoch 81/100\n",
      "31/31 [==============================] - 1s 48ms/step - loss: 16.6363 - mean_absolute_error: 16.9420\n",
      "Epoch 82/100\n",
      "31/31 [==============================] - 1s 47ms/step - loss: 14.8326 - mean_absolute_error: 15.3382\n",
      "Epoch 83/100\n",
      "31/31 [==============================] - 1s 45ms/step - loss: 17.2957 - mean_absolute_error: 17.7789\n",
      "Epoch 84/100\n",
      "31/31 [==============================] - 1s 48ms/step - loss: 15.5364 - mean_absolute_error: 15.9537\n",
      "Epoch 85/100\n",
      "31/31 [==============================] - 1s 48ms/step - loss: 18.0411 - mean_absolute_error: 18.5538\n",
      "Epoch 86/100\n",
      "31/31 [==============================] - 1s 47ms/step - loss: 17.4404 - mean_absolute_error: 17.9663\n",
      "Epoch 87/100\n",
      "31/31 [==============================] - 1s 43ms/step - loss: 16.3951 - mean_absolute_error: 16.8837\n",
      "Epoch 88/100\n",
      "31/31 [==============================] - 1s 46ms/step - loss: 18.5154 - mean_absolute_error: 19.0242\n",
      "Epoch 89/100\n",
      "31/31 [==============================] - 1s 45ms/step - loss: 19.8690 - mean_absolute_error: 20.3885\n",
      "Epoch 90/100\n",
      "31/31 [==============================] - 1s 45ms/step - loss: 18.8249 - mean_absolute_error: 19.2533\n",
      "Epoch 91/100\n",
      "31/31 [==============================] - 1s 45ms/step - loss: 17.3831 - mean_absolute_error: 17.7658\n",
      "Epoch 92/100\n",
      "31/31 [==============================] - 1s 45ms/step - loss: 18.4997 - mean_absolute_error: 19.0077\n",
      "Epoch 93/100\n",
      "31/31 [==============================] - 1s 42ms/step - loss: 17.1347 - mean_absolute_error: 17.5533\n",
      "Epoch 94/100\n",
      "31/31 [==============================] - 1s 45ms/step - loss: 17.9150 - mean_absolute_error: 18.4006\n",
      "Epoch 95/100\n",
      "31/31 [==============================] - 1s 46ms/step - loss: 17.2080 - mean_absolute_error: 17.6729\n",
      "Epoch 96/100\n",
      "31/31 [==============================] - 2s 49ms/step - loss: 18.8637 - mean_absolute_error: 19.1972\n",
      "Epoch 97/100\n",
      "31/31 [==============================] - 1s 45ms/step - loss: 29.6408 - mean_absolute_error: 30.2390\n",
      "Epoch 98/100\n",
      "31/31 [==============================] - 1s 46ms/step - loss: 20.5025 - mean_absolute_error: 20.9954\n",
      "Epoch 99/100\n",
      "31/31 [==============================] - 1s 48ms/step - loss: 17.3945 - mean_absolute_error: 17.8948\n",
      "Epoch 100/100\n",
      "31/31 [==============================] - 1s 47ms/step - loss: 28.7453 - mean_absolute_error: 28.8798\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(train_set, epochs=100, callbacks=[lr_schedule])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plt.xlabel('epoch')\n",
    "plt.ylabel('loss')\n",
    "plt.plot(np.arange(len(history.history['loss'])), history.history['loss'])\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.xlabel('epoch')\n",
    "plt.ylabel('lr')\n",
    "plt.plot(np.arange(len(history.history['lr'])), history.history['lr'])\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.xlabel('lr')\n",
    "plt.ylabel('log(epoch)')\n",
    "plt.semilogx(history.history['lr'], history.history['loss'])\n",
    "plt.axis([1e-8, 1e-4, 0, 30])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Epoch 1/100\n",
      "31/31 [==============================] - 4s 120ms/step - loss: 37.1249 - mean_absolute_error: 37.6446\n",
      "Epoch 2/100\n",
      "31/31 [==============================] - 2s 58ms/step - loss: 36.4398 - mean_absolute_error: 36.9588\n",
      "Epoch 3/100\n",
      "31/31 [==============================] - 2s 57ms/step - loss: 35.6426 - mean_absolute_error: 36.1607\n",
      "Epoch 4/100\n",
      "31/31 [==============================] - 2s 59ms/step - loss: 34.7910 - mean_absolute_error: 35.3067\n",
      "Epoch 5/100\n",
      "31/31 [==============================] - 2s 59ms/step - loss: 33.8925 - mean_absolute_error: 34.4077\n",
      "Epoch 6/100\n",
      "31/31 [==============================] - 2s 58ms/step - loss: 32.9446 - mean_absolute_error: 33.4548\n",
      "Epoch 7/100\n",
      "31/31 [==============================] - 2s 57ms/step - loss: 31.9500 - mean_absolute_error: 32.4574\n",
      "Epoch 8/100\n",
      "31/31 [==============================] - 2s 60ms/step - loss: 30.9065 - mean_absolute_error: 31.4087\n",
      "Epoch 9/100\n",
      "31/31 [==============================] - 2s 60ms/step - loss: 29.8134 - mean_absolute_error: 30.3100\n",
      "Epoch 10/100\n",
      "31/31 [==============================] - 2s 60ms/step - loss: 28.6859 - mean_absolute_error: 29.1747\n",
      "Epoch 11/100\n",
      "31/31 [==============================] - 2s 58ms/step - loss: 27.5520 - mean_absolute_error: 28.0357\n",
      "Epoch 12/100\n",
      "31/31 [==============================] - 2s 56ms/step - loss: 26.4325 - mean_absolute_error: 26.9083\n",
      "Epoch 13/100\n",
      "31/31 [==============================] - 2s 54ms/step - loss: 25.3515 - mean_absolute_error: 25.8211\n",
      "Epoch 14/100\n",
      "31/31 [==============================] - 2s 59ms/step - loss: 24.3201 - mean_absolute_error: 24.7843\n",
      "Epoch 15/100\n",
      "31/31 [==============================] - 2s 60ms/step - loss: 23.3738 - mean_absolute_error: 23.8341\n",
      "Epoch 16/100\n",
      "31/31 [==============================] - 2s 61ms/step - loss: 22.5218 - mean_absolute_error: 22.9785\n",
      "Epoch 17/100\n",
      "31/31 [==============================] - 2s 58ms/step - loss: 21.7304 - mean_absolute_error: 22.1849\n",
      "Epoch 18/100\n",
      "31/31 [==============================] - 2s 56ms/step - loss: 20.9731 - mean_absolute_error: 21.4246\n",
      "Epoch 19/100\n",
      "31/31 [==============================] - 2s 58ms/step - loss: 20.2494 - mean_absolute_error: 20.6979\n",
      "Epoch 20/100\n",
      "31/31 [==============================] - 2s 59ms/step - loss: 19.5808 - mean_absolute_error: 20.0261\n",
      "Epoch 21/100\n",
      "31/31 [==============================] - 2s 59ms/step - loss: 18.9838 - mean_absolute_error: 19.4264\n",
      "Epoch 22/100\n",
      "31/31 [==============================] - 2s 59ms/step - loss: 18.4484 - mean_absolute_error: 18.8851\n",
      "Epoch 23/100\n",
      "31/31 [==============================] - 2s 58ms/step - loss: 18.0012 - mean_absolute_error: 18.4332\n",
      "Epoch 24/100\n",
      "31/31 [==============================] - 2s 59ms/step - loss: 17.6697 - mean_absolute_error: 18.0987\n",
      "Epoch 25/100\n",
      "31/31 [==============================] - 2s 60ms/step - loss: 17.4582 - mean_absolute_error: 17.8865\n",
      "Epoch 26/100\n",
      "31/31 [==============================] - 2s 60ms/step - loss: 17.3434 - mean_absolute_error: 17.7689\n",
      "Epoch 27/100\n",
      "31/31 [==============================] - 2s 63ms/step - loss: 17.2830 - mean_absolute_error: 17.7103\n",
      "Epoch 28/100\n",
      "31/31 [==============================] - 2s 59ms/step - loss: 17.2478 - mean_absolute_error: 17.6769\n",
      "Epoch 29/100\n",
      "31/31 [==============================] - 2s 59ms/step - loss: 17.2221 - mean_absolute_error: 17.6505\n",
      "Epoch 30/100\n",
      "31/31 [==============================] - 2s 59ms/step - loss: 17.1976 - mean_absolute_error: 17.6256\n",
      "Epoch 31/100\n",
      "31/31 [==============================] - 2s 54ms/step - loss: 17.1711 - mean_absolute_error: 17.5994\n",
      "Epoch 32/100\n",
      "31/31 [==============================] - 2s 57ms/step - loss: 17.1414 - mean_absolute_error: 17.5701\n",
      "Epoch 33/100\n",
      "31/31 [==============================] - 2s 61ms/step - loss: 17.1079 - mean_absolute_error: 17.5370\n",
      "Epoch 34/100\n",
      "31/31 [==============================] - 2s 61ms/step - loss: 17.0698 - mean_absolute_error: 17.4993\n",
      "Epoch 35/100\n",
      "31/31 [==============================] - 2s 61ms/step - loss: 17.0264 - mean_absolute_error: 17.4564\n",
      "Epoch 36/100\n",
      "31/31 [==============================] - 2s 59ms/step - loss: 16.9767 - mean_absolute_error: 17.4073\n",
      "Epoch 37/100\n",
      "31/31 [==============================] - 2s 60ms/step - loss: 16.9196 - mean_absolute_error: 17.3508\n",
      "Epoch 38/100\n",
      "31/31 [==============================] - 2s 61ms/step - loss: 16.8538 - mean_absolute_error: 17.2857\n",
      "Epoch 39/100\n",
      "31/31 [==============================] - 2s 61ms/step - loss: 16.7780 - mean_absolute_error: 17.2105\n",
      "Epoch 40/100\n",
      "31/31 [==============================] - 2s 59ms/step - loss: 16.6911 - mean_absolute_error: 17.1246\n",
      "Epoch 41/100\n",
      "31/31 [==============================] - 2s 59ms/step - loss: 16.5916 - mean_absolute_error: 17.0266\n",
      "Epoch 42/100\n",
      "31/31 [==============================] - 2s 59ms/step - loss: 16.4767 - mean_absolute_error: 16.9130\n",
      "Epoch 43/100\n",
      "31/31 [==============================] - 2s 59ms/step - loss: 16.3455 - mean_absolute_error: 16.7833\n",
      "Epoch 44/100\n",
      "31/31 [==============================] - 2s 57ms/step - loss: 16.2016 - mean_absolute_error: 16.6408\n",
      "Epoch 45/100\n",
      "31/31 [==============================] - 2s 60ms/step - loss: 16.0419 - mean_absolute_error: 16.4829\n",
      "Epoch 46/100\n",
      "31/31 [==============================] - 2s 54ms/step - loss: 15.8602 - mean_absolute_error: 16.3030\n",
      "Epoch 47/100\n",
      "31/31 [==============================] - 2s 58ms/step - loss: 15.6621 - mean_absolute_error: 16.1046\n",
      "Epoch 48/100\n",
      "31/31 [==============================] - 2s 59ms/step - loss: 15.4526 - mean_absolute_error: 15.8939\n",
      "Epoch 49/100\n",
      "31/31 [==============================] - 2s 61ms/step - loss: 15.2673 - mean_absolute_error: 15.7096\n",
      "Epoch 50/100\n",
      "31/31 [==============================] - 2s 57ms/step - loss: 15.1059 - mean_absolute_error: 15.5508\n",
      "Epoch 51/100\n",
      "31/31 [==============================] - 2s 57ms/step - loss: 14.9608 - mean_absolute_error: 15.4067\n",
      "Epoch 52/100\n",
      "31/31 [==============================] - 2s 56ms/step - loss: 14.8402 - mean_absolute_error: 15.2875\n",
      "Epoch 53/100\n",
      "31/31 [==============================] - 2s 59ms/step - loss: 14.7498 - mean_absolute_error: 15.2002\n",
      "Epoch 54/100\n",
      "31/31 [==============================] - 2s 57ms/step - loss: 14.6656 - mean_absolute_error: 15.1175\n",
      "Epoch 55/100\n",
      "31/31 [==============================] - 2s 58ms/step - loss: 14.5918 - mean_absolute_error: 15.0451\n",
      "Epoch 56/100\n",
      "31/31 [==============================] - 2s 55ms/step - loss: 14.5280 - mean_absolute_error: 14.9833\n",
      "Epoch 57/100\n",
      "31/31 [==============================] - 2s 60ms/step - loss: 14.4649 - mean_absolute_error: 14.9215\n",
      "Epoch 58/100\n",
      "31/31 [==============================] - 2s 60ms/step - loss: 14.4039 - mean_absolute_error: 14.8600\n",
      "Epoch 59/100\n",
      "31/31 [==============================] - 2s 62ms/step - loss: 14.3467 - mean_absolute_error: 14.8020\n",
      "Epoch 60/100\n",
      "31/31 [==============================] - 2s 58ms/step - loss: 14.2918 - mean_absolute_error: 14.7458\n",
      "Epoch 61/100\n",
      "31/31 [==============================] - 2s 56ms/step - loss: 14.2361 - mean_absolute_error: 14.6902\n",
      "Epoch 62/100\n",
      "31/31 [==============================] - 2s 58ms/step - loss: 14.1668 - mean_absolute_error: 14.6202\n",
      "Epoch 63/100\n",
      "31/31 [==============================] - 2s 58ms/step - loss: 14.0843 - mean_absolute_error: 14.5378\n",
      "Epoch 64/100\n",
      "31/31 [==============================] - 2s 60ms/step - loss: 13.9938 - mean_absolute_error: 14.4484\n",
      "Epoch 65/100\n",
      "31/31 [==============================] - 2s 58ms/step - loss: 13.8766 - mean_absolute_error: 14.3291\n",
      "Epoch 66/100\n",
      "31/31 [==============================] - 2s 61ms/step - loss: 13.6678 - mean_absolute_error: 14.1192\n",
      "Epoch 67/100\n",
      "31/31 [==============================] - 2s 59ms/step - loss: 10.9936 - mean_absolute_error: 11.5070\n",
      "Epoch 68/100\n",
      "31/31 [==============================] - 2s 58ms/step - loss: 6.8307 - mean_absolute_error: 7.3000\n",
      "Epoch 69/100\n",
      "31/31 [==============================] - 2s 58ms/step - loss: 7.1856 - mean_absolute_error: 7.6626\n",
      "Epoch 70/100\n",
      "31/31 [==============================] - 2s 60ms/step - loss: 6.2037 - mean_absolute_error: 6.6262\n",
      "Epoch 71/100\n",
      "31/31 [==============================] - 2s 59ms/step - loss: 6.3743 - mean_absolute_error: 6.8393\n",
      "Epoch 72/100\n",
      "31/31 [==============================] - 2s 61ms/step - loss: 6.4771 - mean_absolute_error: 6.9404\n",
      "Epoch 73/100\n",
      "31/31 [==============================] - 2s 59ms/step - loss: 6.5950 - mean_absolute_error: 7.0566\n",
      "Epoch 74/100\n",
      "31/31 [==============================] - 2s 58ms/step - loss: 7.0622 - mean_absolute_error: 7.4396\n",
      "Epoch 75/100\n",
      "31/31 [==============================] - 2s 59ms/step - loss: 9.1516 - mean_absolute_error: 9.6299\n",
      "Epoch 76/100\n",
      "31/31 [==============================] - 2s 58ms/step - loss: 9.3292 - mean_absolute_error: 9.7901\n",
      "Epoch 77/100\n",
      "31/31 [==============================] - 2s 58ms/step - loss: 7.0579 - mean_absolute_error: 7.5393\n",
      "Epoch 78/100\n",
      "31/31 [==============================] - 2s 56ms/step - loss: 8.0962 - mean_absolute_error: 8.5827\n",
      "Epoch 79/100\n",
      "31/31 [==============================] - 2s 55ms/step - loss: 13.4396 - mean_absolute_error: 13.9207\n",
      "Epoch 80/100\n",
      "31/31 [==============================] - 2s 58ms/step - loss: 7.6265 - mean_absolute_error: 8.1090\n",
      "Epoch 81/100\n",
      "31/31 [==============================] - 2s 58ms/step - loss: 8.1300 - mean_absolute_error: 8.6261\n",
      "Epoch 82/100\n",
      "31/31 [==============================] - 2s 61ms/step - loss: 8.4520 - mean_absolute_error: 8.8888\n",
      "Epoch 83/100\n",
      "31/31 [==============================] - 2s 60ms/step - loss: 8.0857 - mean_absolute_error: 8.5397\n",
      "Epoch 84/100\n",
      "31/31 [==============================] - 2s 60ms/step - loss: 7.2707 - mean_absolute_error: 7.7268\n",
      "Epoch 85/100\n",
      "31/31 [==============================] - 2s 56ms/step - loss: 8.1350 - mean_absolute_error: 8.6013\n",
      "Epoch 86/100\n",
      "31/31 [==============================] - 2s 56ms/step - loss: 10.7244 - mean_absolute_error: 11.2489\n",
      "Epoch 87/100\n",
      "31/31 [==============================] - 2s 59ms/step - loss: 9.0058 - mean_absolute_error: 9.5083\n",
      "Epoch 88/100\n",
      "31/31 [==============================] - 2s 58ms/step - loss: 11.1106 - mean_absolute_error: 11.6002\n",
      "Epoch 89/100\n",
      "31/31 [==============================] - 2s 62ms/step - loss: 7.6442 - mean_absolute_error: 8.1171\n",
      "Epoch 90/100\n",
      "31/31 [==============================] - 2s 55ms/step - loss: 11.2422 - mean_absolute_error: 11.6865\n",
      "Epoch 91/100\n",
      "31/31 [==============================] - 2s 54ms/step - loss: 9.2837 - mean_absolute_error: 9.7915\n",
      "Epoch 92/100\n",
      "31/31 [==============================] - 2s 58ms/step - loss: 9.6524 - mean_absolute_error: 10.0413\n",
      "Epoch 93/100\n",
      "31/31 [==============================] - 2s 58ms/step - loss: 10.7227 - mean_absolute_error: 11.2438\n",
      "Epoch 94/100\n",
      "31/31 [==============================] - 2s 58ms/step - loss: 13.6016 - mean_absolute_error: 14.1295\n",
      "Epoch 95/100\n",
      "31/31 [==============================] - 2s 57ms/step - loss: 12.6379 - mean_absolute_error: 13.0817\n",
      "Epoch 96/100\n",
      "31/31 [==============================] - 2s 56ms/step - loss: 15.2960 - mean_absolute_error: 15.7711\n",
      "Epoch 97/100\n",
      "31/31 [==============================] - 2s 57ms/step - loss: 17.6081 - mean_absolute_error: 18.0619\n",
      "Epoch 98/100\n",
      "31/31 [==============================] - 2s 58ms/step - loss: 16.7035 - mean_absolute_error: 17.1897\n",
      "Epoch 99/100\n",
      "31/31 [==============================] - 2s 56ms/step - loss: 15.4705 - mean_absolute_error: 15.9821\n",
      "Epoch 100/100\n",
      "31/31 [==============================] - 2s 60ms/step - loss: 28.3565 - mean_absolute_error: 28.8513\n"
     ]
    }
   ],
   "source": [
    "# 可以发现学习率设置为1e-6到1e-5之间较为合适\n",
    "model.compile(\n",
    "    loss=tf.keras.losses.Huber(),\n",
    "    optimizer=tf.keras.optimizers.SGD(lr=5e-5, momentum=0.9),\n",
    "    metrics=[tf.keras.metrics.MeanAbsoluteError()]\n",
    ")\n",
    "history = model.fit(train_set, epochs=100, callbacks=[lr_schedule]) # 固定学习率，重新训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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\n"
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6PWFBcBzH9hLIA7vIxTYysYtcbDMYg75SvukuHMJ8qFQqtpdAHthFLraRiV3kYpudG/RJ+YD5LhzCiIiIaOgN+qT8IHAI8yGTydheAnlgF7nYRiZ2kYttds7ESfmmu3AIIyIioqFXu7A40PPBgsAhzIdyuWx7CeSBXeRiG5nYRS622bnawkUkZx8c6HOa7sIhjIiIiIZae7WKzmoN8f17bC9lSziE+ZBOp20vgTywi1xsIxO7yMU2O1NbuIjkQ/dDhQY71pjuwiHMh3A4bHsJ5IFd5GIbmdhFLrbZGROHIgHzXTiE+VAqlWwvgTywi1xsIxO7yMU2O1M9fwGphx8Y+POa7sIhjIiIiIaWdpzelfIfGvwQZhqHMB9SqZTtJZAHdpGLbWRiF7nYZvvW3ruC6HQW4UR84M9tustALiurlHoaQBnArNb6RT+Pb+G+wwBmAUBr/ZVBrHerYrGYjc3SJthFLraRiV3kYpvtqy1cQOrhwZ8PBpjvsuM9Ye7gBK31Sff2sc0e93uf+xRfdIevKaXU7E7Xux3Ly8s2NkubYBe52EYmdpGLbbavunARyVkzhyJNdxnE4cinACy4Hy8AOOzjcV/3KaWeAXBaKTWrtX5Ra70AIiIiIpfJPWGmDeJwZOaO29M+Hvd7X/+5VpRSXwbwrNa6vP6Tzp49i+eeew7tdhtHjhzB0aNHMT09jWaziWq1CgDIZrNwHOfmu6H33wuqfyXcdDqNcDh886cgUqkUYrHYzQm4Xq+jWq2iWCwCABKJBFKpFIrFIrTWiMfjSKVSKJVKcBwH0WgU6XQa5XIZnU4HkUgEmUwGlUoFrVYL4XAY2WwW1WoVjUYDSinMzMygWq2iXq8DAGZmZlCv11Gr1XpfjAG/pmQyiUQiMdSvqdFoYHV1daRe06h0chwHhUJhpF7TKHTqdDo3u4zKaxqVTv01jtJrCqJTu1xBe62B9mQcxcXFgb+m/pp2+prOnDmDU6dOYW1tDQBycCmtNXZCKfUCgBNa65PuIcTjWutnN3rcfcjvfee11i+6e8UyWusvrd9+Pp/X+Xx+R69hM9VqlSdNCsQucrGNTOwiF9tsT+m1v8TyN17F+/7xzxp5fhNdlFLPa63zwGAOR57Grb1YswBO+Hh8K/f1ZdA7aT9w/WmcZGEXudhGJnaRi222x9RFWvtMd9nxEOaeND/r7r3KrDux/sS9Ht/ifZn+SfpeP3lJRERE48nURVqDMpBLVKw7RHhy3X3HN3l82/cFLZFI2No0bYBd5GIbmdhFLrbZum67g/riFUw+eMjYNkx34cVafeBxepnYRS62kYld5GKbrVu7eAmxfXsQjpu7lpfpLhzCfOCxepnYRS62kYld5GKbrastXDR+aQrx54SNg53+BCmZwS5ysY1M7CIX22xddeGCsYu09pnuwiHMh3h88O9HRTvHLnKxjUzsIhfbbI3WOpA9Yaa7cAjzgcfqZWIXudhGJnaRi222prVShu52EZ3OGt0OzwkToH8FXJKFXeRiG5nYRS622ZrawkWkZh+AUsrodkx34RDmg+M4tpdAHthFLraRiV3kYputqZ6/YPQirX2mu3AI8yEajdpeAnlgF7nYRiZ2kYtttqZ2Ppg37TbdhUOYD+l02vYSyAO7yMU2MrGLXGzjX6e2hsZSEZM5cxdp7TPdhUOYD/13SSdZ2EUutpGJXeRiG/+q7xSQfOgBhCIDedOfDZnuwiHMh06nY3sJ5IFd5GIbmdhFLrbxb/Wt89j12Gwg2zLdZeiHsEKhgHw+j/n5eWPbiAQwbdPWsYtcbCMTu8jFNv6tnlvArkeDGcIG3cWdVXI3n3+gz25BLpdDPp83uo1MJmP0+Wl72EUutpGJXeRiG386tTU0r68gGcD5YMDgu8zNzQFAoX976PeEBaFSqdheAnlgF7nYRiZ2kYtt/Km+/S5Ssw9ChcOBbM90Fw5hPrRaLdtLIA/sIhfbyMQucrGNP0GeDwaY78IhzIdwQBM3bQ27yMU2MrGLXGzjz+q5hUCHMNNdOIT5kM2afW8q2h52kYttZGIXudhmc51qDc1iCZMP3BfYNk134RDmQ7Vatb0E8sAucrGNTOwiF9tsbvXcu0i9LxfY+WCA+S4cwnxoNBq2l0Ae2EUutpGJXeRim82tvnU+sEtT9JnuwiHMB9Pv0k7bwy5ysY1M7CIX22wu6PPBAPNdOIT5MDMzY3sJ5IFd5GIbmdhFLrbZWHu1inbpBibvPxjodk134RDmA4/Vy8QucrGNTOwiF9tsrGrhfDCA54SJUK/XbS+BPLCLXGwjE7vIxTYbW33rPFIBnw8GmO/CIYyIiIhEs3E+WBA4hPnAY/UysYtcbCMTu8jFNvfWrqyifaMS+PlgAM8JE4G7iWViF7nYRiZ2kYtt7q13fbCHoELBjyw8HClArVazvQTywC5ysY1M7CIX29ybjeuD9ZnuwiGMiIiIxFp9azTPBwNGYAgrFArI5/OYn583to3p6Wljz03bxy5ysY1M7CIX23hrFlfgrNWROHTAyvYH3cWdVXL920M/hOVyOeTzeczNzRnbRrPZNPbctH3sIhfbyMQucrGNt8ob55B+4hEr54MBg+/iziqF/u2hH8KCwIvoycQucrGNTOwiF9t4u/HGOex+4jFr2+fFWomIiGjsdDsdrL51HunHH7G9FGM4hPmQzWZtL4E8sItcbCMTu8jFNnernb+A+P69iKSS1tZguguHMB8cx7G9BPLALnKxjUzsIhfb3O3G2XPY/cSjVtdguguHMB8qlYrtJZAHdpGLbWRiF7nY5m6VN99C2vIQZroLhzAiIiISpVW6gVapguRD99teilEcwnzIZDK2l0Ae2EUutpGJXeRim9tV3jyH9Pe9z9qlKfpMd+EQRkRERKLcOHvO+qHIIHAI86FcLtteAnlgF7nYRiZ2kYttbtGOg9W/etv6SfmA+S4cwoiIiEiM2rvvITqdxcTutO2lGMchzId0evT/QxhG7CIX28jELnKxzS03zr5l9Sr565nuwiHMh3A4bHsJ5IFd5GIbmdhFLra5pfd+kfYPRQLmu3AI86FUKtleAnlgF7nYRiZ2kYttetqVKprXl5F6+EHbSwFgvss9hzCl1C8Z3TIRERHROpU3z2HX+x+GGpM9gxvtCVtYf0Mp9aTZpciVSqVsL4E8sItcbCMTu8jFNj29Q5EyzgcDzHfZaAj7h0qpt5VSv62U+h0ALxldyTYVCgXk83nMz88b20YsFjP23LR97CIX28jELnKxDaC7XVTelHFpir5Bd3FnlVz/dmSDz31Ba/1y/4ZS6m8MdCUDksvlkM/njW5jeXkZu3btMroN2jp2kYttZGIXudgGqL5TwEQ2jehUxvZSbhp0l7m5OQAo9G/fc0/Y+gHMdX5gqyAiIiJap3zmDWSefML2MgJ1154wpdSfAej/OIACoN3fPwzgkeCWJkcymbS9BPLALnKxjUzsIte4t9Fao3zmDbzvF37W9lJuY7qL1+HIFzz2gkEp9WGjKxEskUjYXgJ5YBe52EYmdpFr3NvU37sMFQojfnCf7aXcxnSXuw5Heg1g7v3fMboSwYrFou0lkAd2kYttZGIXuca9Tf9QpFLK9lJuY7oLL9ZKREREVpW+8wYyHx6v88EADmG+jPtuYqnYRS62kYld5BrnNo1rRXSqa0g+dL/tpdwl8MORdDdeRE8mdpGLbWRiF7nGuU3vUOTjUCF5I4nNi7WSa9yP1UvFLnKxjUzsItc4tyl/5w1khR6K5DlhAmitbS+BPLCLXGwjE7vINa5tWuUKGteuI/XorO2leDLdhUOYD/F43PYSyAO7yMU2MrGLXOPa5sbrb2L3Bx5DKLLRG/jYY7oLhzAfxvlYvWTsIhfbyMQuco1rG+k/FTkU54QppZ5WSh1TSj3j93G/96177IVBrHU7SqXS5p9EgWMXudhGJnaRaxzbdGprqL17EeknHrO9lHsy3WXHQ5hS6mkA0FqfdG8f2+xxv/ete45jAKwdMHYcx9amaQPsIhfbyMQuco1jmxt/+RZ2PfYwwrGo7aXck+kug9gT9hSABffjBQCHfTzu9z4opWbX3W9FNCr3P5Bxxi5ysY1M7CLXOLYpf+es2J+K7DPdZRBnwmXuuD3t43G/9wHArNb65L3eyuDs2bN47rnn0G63ceTIERw9ehTT09NoNpuoVqsAgGw2C8dxUKlUegvK9DZVLpcBAOl0GuFw+OZux1QqhVgshuXlZQC9n46oVqs3f1Q1kUgglUqhWCxCa414PI5UKoVSqQTHcRCNRpFOp1Eul9HpdBCJRJDJZFCpVNBqtRAOh5HNZlGtVtFoNKCUwszMDKrVKur1OgBgZmYG9XodtVqt98UY8GtKJpNIJBJD/ZoAYHV1daRe06h00lqjUCiM1GsahU7dbvdml1F5TaPSqVQqodVqjdRr2qiTcrq48b23oeaegi4Wxb6mfped/rd35swZnDp1CmtrawCQg0vt9Mcv3XO1TriD0jEAx7XWz270uPuQn/tOrDs8+ZLW+qfu3H4+n9f5fH5Hr2Ezi4uLOHTokNFt0Naxi1xsIxO7yDVubVZOv47lb3wbj3zh52wvZUMmuiilntda54HB7Ak7jVt7sWYBnPDxeMbnfSvuQJYBMKuUOqy1fm0Aa96STqcT9CbJB3aRi21kYhe5xq3NyqkzmProk7aXsSnTXXZ8TpjW+ivoDUjHAGTW7bk6ca/Ht3Dfa+7zTeHuw5WBiQi9fsm4Yxe52EYmdpFrnNp0qjVU334XmQ9/wPZSNmW6y0CeXWv9JffDk+vuO77J477uc+9/EcCLg1jrdvSP/ZIs7CIX28jELnKNU5vSq3+J9AceQzges72UTZnuwou1+tA/EY9kYRe52EYmdpFrnNos/8V3huJQJGC+C4cwH1qtlu0lkAd2kYttZGIXucalTbO4gua1InY/8ajtpfhiuguHMB/C4bDtJZAHdpGLbWRiF7nGpc3KqdeRPfJBqCF5vaa7cAjzIZvN2l4CeWAXudhGJnaRaxzaaK2xcmp4DkUC5rtwCPOhfwE6koVd5GIbmdhFrnFoU1+8gm6rjeTDD9peim+mu3AI86HRaNheAnlgF7nYRiZ2kWsc2qy4J+Tf6x1wJDLdhUOYD8P0H8w4YRe52EYmdpFr1Nvobhcrp18fqkORgPkuHMJ8mJmZsb0E8sAucrGNTOwi16i3WT23gMiuJBIH99leypaY7sIhzIdxOFY/jNhFLraRiV3kGvU2vbcp+rDtZWwZzwkToP8u6yQLu8jFNjKxi1yj3KbbbqP8nTcw9dSHbC9ly0x34RBGRERExpTPvInJ+w8imt1teynicAjzYdSP1Q8rdpGLbWRiF7lGuU3xlVOY+cRHbS9jW3hO2CYKhQLy+Tzm5+eNbWOUdxMPM3aRi21kYhe5RrVN41oR9ctXkXnyCdtL2ZZBd3FnlVz/9tAPYblcDvl8HnNzc8a2UavVjD03bR+7yMU2MrGLXKPapvj105g+egShiYjtpWzLoLu4s0qhf3vohzAiIiKSp9vpYPmbr2LmE0/ZXopYHMJ8mJ6etr0E8sAucrGNTOwi1yi2ufH6m4gf2Iv4vj22l7JtprtwCPOh2WzaXgJ5YBe52EYmdpFrFNtcf+UU9vzQx2wvY0dMd+EQ5sOoX0RvWLGLXGwjE7vINWptGktF1BevDO0J+X28WCsRERENld4J+YeH9oT8oHAI8yGbzdpeAnlgF7nYRiZ2kWuU2tw8If8Hh/PaYOuZ7sIhzAfHcWwvgTywi1xsIxO7yDVKbW68/j3E9+9FfP/wnpDfZ7oLhzAfKpWK7SWQB3aRi21kYhe5RqnN9VdOYc+QXiH/Tqa7cAgjIiKigWheX0b9vcvIfHi4T8gPCocwHzKZjO0lkAd2kYttZGIXuUalzdL8NzH98SMITUzYXspAmO7CIYyIiIh2zKk3sPzN17D3k99veylDg0OYD+Vy2fYSyAO7yMU2MrGLXKPQpvj1U0g//giiUxnbSxkY0104hBEREdGOaMfB0n//BvYd+4TtpQwVDmE+pNNp20sgD+wiF9vIxC5yDXub0mtnEZ3OIpk7ZHspA2W6C4cwH8LhsO0lkAd2kYttZGIXuYa5jdYa1068gn3HR28vmOkuQz+EFQoF5PN5zM/PG9tGqVQy9ty0fewiF9vIxC5yDXOb6jsFOPUGdn/w/baXMnCD7uLOKrn+7aF/U6dcLod8Pm97GURERGPp2olXsO/YD0KFhn6/jnFzc3MAUOjf5lfMh1QqZXsJ5IFd5GIbmdhFrmFt07hWRG3hAqY/ftj2Uoww3YVDmA+xWMz2EsgDu8jFNjKxi1zD2mbp5a9j5hMfQygatb0UI0x34RDmw/Lysu0lkAd2kYttZGIXuYaxTadaw8rp17F37uO2l2KM6S4cwoiIiGjLrv+PbyHz5OOY2L3L9lKGFocwH5LJpO0lkAd2kYttZGIXuYatjVNvYOm/fwP7f3TO9lKMMt2FQ5gPiUTC9hLIA7vIxTYysYtcw9bm2stfR/oDjyG+b4/tpRhluguHMB+KxaLtJZAHdpGLbWRiF7mGqU1nrY7rX/smDnz6k7aXYpzpLhzCiIiIyLell/8ndn/wMcT3zdheytDjEObDsO0mHhfsIhfbyMQucg1Lm85aHUtf+wb2j8FeMICHI0UY1ovojTp2kYttZGIXuYalzdLLX0fmr30f4nvHYy8YL9YqwDAdqx8n7CIX28jELnINQ5tObQ1LX/smDvzYeOwFA3hOmAhaa9tLIA/sIhfbyMQucg1Dm6WXv47Mk48jtmfa9lICY7oLhzAf4vG47SWQB3aRi21kYhe5pLfp1NawNP8tHPjUD9teSqBMd+EQ5sOwHKsfN+wiF9vIxC5ySW9z7cQrY7cXDOA5YZsqFArI5/OYn583to1SqWTsuWn72EUutpGJXeSS3Ka1Usb1P/8LHPxbx2wvJXCD7uLOKrn+7chAn92CXC6HfD5vdBuO4xh9ftoedpGLbWRiF7kkt7n0X/4Ue37oY4hOZWwvJXCD7jI3NwcAhf7tod8TFoRoNGp7CeSBXeRiG5nYRS6pbWqF97D6vXdG/j0i78V0Fw5hPqTTadtLIA/sIhfbyMQucklso7XG4kv/FQd//EcQjsdsL8cK0104hPlQLpdtL4E8sItcbCMTu8glsU35tbNwGk1Mf/8R20uxxnQXDmE+dDod20sgD+wiF9vIxC5ySWvTbXew+Ht/gkNP/xhUaHxHBdNdxvcruwWRyND//MJIYhe52EYmdpFLWpulr30DiYP7kP6+99leilWmu3AI8yGTydheAnlgF7nYRiZ2kUtSm/ZqFdf+9H/g0E9+2vZSrDPdhUOYD5VKxfYSyAO7yMU2MrGLXJLaXP7DE5j66IcQ37/H9lKsM92FQ5gPrVbL9hLIA7vIxTYysYtcUtpUFy6ifOZNHBjDC7N6Md2FQ5gP4XDY9hLIA7vIxTYysYtcEtpox8GF3/hd3P9TP4ZIctL2ckQw3YVDmA/ZbNb2EsgDu8jFNjKxi1wS2lw78Qqi2d3IPvUh20sRw3QXDmE+VKtV20sgD+wiF9vIxC5y2W7TWCri6p/9OR74mb8DpZTVtUhiuguHMB8ajYbtJZAHdpGLbWRiF7lsttFa4+Jv/gEOfGoOsZkpa+uQyHSXgVwAQyn1NIAygFmt9Yt+Hvdzn1IqA2DW/fWU1vrZQax3q/ivApnYRS62kYld5LLZZuVbr6FTW8PeT/6AtTVIZbrLjveEuYMTtNYn3dvHNnvc730APgPgI1rrr7j3PbPT9W7HzMyMjc3SJthFLraRiV3kstWmvVrF4u/+CR787E9ACfjhAGlMdxnE4cinACy4Hy8AOOzjcV/3aa1fXLdnbXbd44GyfayevLGLXGwjE7vIZavN4kv/FVMfexLJBw9Z2b50prsM4nBk5o7b0z4e93sfAEApNQtgpb+XbL2zZ8/iueeeQ7vdxpEjR3D06FFMT0+j2Wze/OJls1k4jnPzomv9K+D235gznU4jHA6jVCoBAFKpFGKxGJaXlwEAy8vLSCaTKBaLAIBEIoFUKoVisQitNeLxOFKpFEqlEhzHQTQaRTqdRrlcRqfTQSQSQSaTQaVSQavVQjgcRjabRbVaRaPRgFIKMzMzqFarqNfrAHrTd71eR61W630xBvyakskkEonEUL+mlZUVTE5OjtRrGpVO165du/l8o/KaRqHT+i6j8ppGpdPbb7+Ner0e6Guq/eVbqL+9gNlf+jwuX77MTh6vqd9lp6/pzJkzOHXqFNbW1gAgB5fSWmMnlFIvADihtT7pHkI8vv7cLa/H3Yc2va//PEqpX9Zaf8lr+/l8Xufz+R29hs0UCgXkcjmj26CtYxe52EYmdpEr6Dat0g187//8VbzvF34WyYfuD2y7w8ZEF6XU81rrPDCYPWGncWsv1iyAEz4ez/i8D0qpp/sDmFLqsNb6tQGseUt4HoVM7CIX28jELnIF2UZ3uyj8h9/G3k/+AAewTYg/J8w9aX7W3XuVWXdi/Yl7Pe73PvfjF5RSryqlXgVg5Wdn+7srSRZ2kYttZGIXuYJsc+3EK9BOF/t/dC6wbQ4r010GcomKdYcKT6677/gmj296nzvQPTyINe5ErVbDnj18I1Np2EUutpGJXeQKqs3axUu49md/jvd/8RegQrxU6GZMd2EBIiKiMdBttbDw67+F+3/6f+FFWYXgEObD9PSdP/BJErCLXGwjE7vIFUSbxa98FckH78PUR580vq1RYboLhzAfms2m7SWQB3aRi21kYhe5TLdZOXUGN944hwd+5u8Y3c6oMd2FQ5gPvMChTOwiF9vIxC5ymWyz9t5lvPfbf4SH/9FnEU7EjW1nFPENvImIiGhbOtUazv9fv4H7/96PY/LQAdvLoTtwCPMhm83aXgJ5YBe52EYmdpHLRBvd7WLh138L2cMfxNRTHxr4848D098zHMJ8cBzH9hLIA7vIxTYysYtcJtpc+oM/BbTGfX/3bw78uceF6e8ZDmE+9N8rimRhF7nYRiZ2kWvQbVa+/V2Uvv1dzP78/woVDg/0uceJ6e8ZDmFEREQjZO3iJbz3//0XPPwP/z4iqaTt5dAGOIT50H+3dJKFXeRiG5nYRa5BtWleX8Y7v/Yf8cD/9ncx+cB9A3nOcWb6e4ZDGBER0Qhor1bx9q/+B+z/9CeRPfwB28shH4Z+CCsUCsjn85ifnze2jXK5bOy5afvYRS62kYld5NppG6fZwju/9h+RPfJB7J37+GAWRQP/nnFnlVz/9kDewNumXC6HfD5vexlERERWaMfBwou/icSBfTj4t3/E9nJoA3NzcwBQ6N8e+j1hQUin07aXQB7YRS62kYld5NpuG601Lvzm7wMAHvzsT0ApNchljT3T3zMcwnwI88d7RWIXudhGJnaRaztttNZYfOmPUb90FbM//zO8FIUBpr9nOIT5UCqVbC+BPLCLXGwjE7vItdU2Wmss/s4fo/rOBTzyT/8BwvGYoZWNN9PfM0N/ThgREdE4uTmAnb+AR37x5xCZTNheEm0T94T5kEqlbC+BPLCLXGwjE7vI5bcNB7Bgmf6e4Z4wH2Ix7uaViF3kYhuZ2EUuP216A9gfobpwkQNYQEx/z3BPmA/Ly8u2l0Ae2EUutpGJXeTarI12HFz8T7/XG8C+wAEsKKa/Z7gnjIiISDCn2cK7/+4/QztdPPrPfp4n4Y8QDmE+JJN8A1SJ2EUutpGJXeS6V5v2ahXv/Nr/i8SBPXjwsz/Jy1AEzPT3DIcwHxIJ7vaViF3kYhuZ2EUurzbN68t4+1f/H2Q/8iEc/PHjvBCrBaa/Z3hOmA/FYtH2EsgDu8jFNjKxi1x3tqm9+x7e+tf/N/Yd/yHc97d/hAOYJaa/Z7gnjIiISJDi10/j0h/8Nzz42Z9E5kOP214OGcQhzAfuwpeJXeRiG5nYRa5EIoFuu4P3fuePUD23gMd+6fOI799re1ljz/T3DIcwH3iBQ5nYRS62kYld5Ip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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plt.xlabel('epoch')\n",
    "plt.ylabel('loss')\n",
    "plt.plot(np.arange(len(history.history['loss'])), history.history['loss'])\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.xlabel('epoch')\n",
    "plt.ylabel('lr')\n",
    "plt.plot(np.arange(len(history.history['lr'])), history.history['lr'])\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.xlabel('lr')\n",
    "plt.ylabel('log(epoch)')\n",
    "plt.semilogx(history.history['lr'], history.history['loss'])\n",
    "plt.axis([1e-8, 1e-4, 0, 30])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "source": [
    "## Step6: 使用RNN网络模型预测时间序列"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 720x432 with 1 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "forecast = [model.predict(series[time: time + window_size][np.newaxis]) for time in range(len(series) - window_size)]\n",
    "\n",
    "forecast = forecast[split_time - window_size:]\n",
    "results = np.array(forecast)[:, 0, 0]\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plot_series(time_valid, X_valid)\n",
    "plot_series(time_valid, results)\n",
    "plt.show()"
   ]
  },
  {
   "source": [
    "## Step7: 对预测结果进行评估"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "27.285938"
      ]
     },
     "metadata": {},
     "execution_count": 11
    }
   ],
   "source": [
    "tf.keras.metrics.mean_absolute_error(X_valid, results).numpy()"
   ]
  },
  {
   "source": [
    "## Step8: 评估训练结果"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 720x432 with 1 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# 从训练集和测试集上获取结果列表\n",
    "\n",
    "mae = history.history['mean_absolute_error']\n",
    "loss = history.history['loss']\n",
    "epochs = range(len(loss))\n",
    "\n",
    "# 可视化MAE和loss\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(epochs, mae, 'r')\n",
    "plt.plot(epochs, loss, 'b')\n",
    "plt.title('MAE and Loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend(['MAE', 'Loss'])\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(epochs[:20], mae[:20], 'r')\n",
    "plt.plot(epochs[:20], loss[:20], 'b')\n",
    "plt.title('Zoomed MAE and Loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend(['MAE', 'Loss'])\n",
    "plt.show()"
   ]
  }
 ]
}